Despite the mass vaccination effort being in full swing, officials are still concerned about a “fourth wave” in COVID-19 cases. Several states have reported surges since the beginning of spring, with hospitals in states like Michigan quickly approaching full capacity. It is very likely that, even with a vaccine, there will still be a high degree of morbidity and mortality associated with COVID-19 for some time to come.
The majority of these severe cases are likely to come from a core group of at-risk patients. While providers are now better equipped to manage hospitalized COVID-19 patients with antiviral and corticosteroid medications, these treatments are only recommended for severe patients. Treating these individuals before they become severe and require hospitalization is critical if we are to ease the burden on our nation’s hospitals. As a result, physicians must be able to quickly identify and assign the appropriate treatment regimen to each patient based on certain clinical indicators. Data within the electronic health record (EHR) can be leveraged to help physicians make these treatment decisions, but only if these systems are equipped with precise and accurate terminology.
Considerations for monoclonal antibody treatment
One very promising COVID-19 treatment option is monoclonal antibody infusion therapy. Drug combinations, such as Regeneron’s casirivimab and imdevimab and Eli Lily’s bamlanivimab and etesevimab, can be used to treat patients with mild to moderate COVID-19 without the need for hospitalization.* These therapies have been shown to be effective at decreasing COVID-19’s severity and morbidity in at-risk patients and are most effective if given as soon as possible after testing positive for SARS-CoV-2. On the other hand, antibody infusion therapy is not recommended for all patients and is contraindicated in those experiencing severe complications from COVID-19 or who require supplemental oxygen or mechanical ventilation. Thus, physicians must carefully determine whether monoclonal antibody treatment is indicated for a specific patient, and they must make this determination as quickly as possible to maximize its therapeutic potential.
Using value sets to identify at-risk patients
Tools that automatically stratify patients based on certain criteria could be used to drive clinical decision support (CDS) engines to help physicians determine which patients would most likely benefit from monoclonal antibody therapy. However, these engines are only as accurate as the underlying terminology used to drive them. For instance, determining whether someone qualifies for Regeneron as an “at-risk” patient depends on whether they have an underlying condition associated with poor COVID-19 prognosis (e.g., hypertension), as well as a combination of other factors, such as age, immunocompromised status, and body mass index (BMI).
Highly granular, precisely defined value sets based on clinically relevant and specific terminology could be used to identify eligible patients quickly and accurately, saving providers valuable time and energy that would otherwise be spent manually combing through the EHR to find this important data. In addition, by using these value sets providers can more effectively target appropriate treatment regimens for their patients, reducing the need for hospitalization and improving outcomes in this vulnerable population.
Learn more about how IMO is helping in the fight against COVID-19, click here.
* Monoclonal antibody infusion therapy can be administered in outpatient settings such as skilled nursing facilities.
